Geospatial visualization

Geospatial visualizations

• Earliest form of information visualizations
• Geospatial data visualizations
• Google Maps

Designing modern maps

• Depict spatial features
• Incorporate additional attributes and information
• Major features
  • Scale
  • Projection
  • Symbols

Scale

• Proportion between distances and sizes on a map and their actual distances and sizes on Earth
• Small-scale map
• Large-scale map

Large-scale map

Small-scale map

Projection

• Process of taking a three-dimensional object and visualizing it on a two-dimensional surface
• No 100% perfect method for this
• Always introduces distortions
• Properties of projection methods
  1. Shape
  2. Area
  3. Angles
  4. Distance
  5. Direction

Conformal projections

Mollweide

Symbols

Map data file formats

• Vector files
  • Raster images
  • Numeric data
• Popular formats
  • Shapefile
  • GeoJSON

Shapefile

• Encodes points, lines, and polygons
• Collection of files
  • .shp - geographic coordinates
  • .dbf - data associated with the geographic features
  • .prj - projection of the coordinates in the shapefile

GeoJSON

• Uses JavaScript Object Notation (JSON) file format

  {
    "type": "Feature",
    "geometry": {
      "type": "Point",
      "coordinates": [125.6, 10.1]
    },
    "properties": {
      "name": "Dinagat Islands"
    }
  }

• Plain text files

Simple features

• Packages in R for spatial data
• Tidy packages for spatial data
• Simple features and sf
  • Emphasizes spatial geometry
  • Describes how to store and retrieve objects
  • Defines geometrical operations

What is a feature?

• Thing or an object in the real world
• Sets of features
• Geometry
• Attributes

Dimensions

• Geometries composed of points
  • Coordinates in a 2-, 3- or 4-dimensional space
  • All points in a geometry have the same dimensionality
• X and Y coordinates
• Z coordinate
• M coordinate (measure associated with point rather than the feature)
• Four possible cases
  1. XY
  2. XYZ
  3. XYM
  4. XYZM

Simple feature geometry types

Simple features in R

• Uses basic R data structures
• Data frame with one row per feature
• Lots of list columns

Import USA state boundaries

usa <- st_read("../data/census_bureau/cb_2013_us_state_20m/cb_2013_us_state_20m.shp")

## Reading layer `cb_2013_us_state_20m' from data source `/Users/soltoffbc/Projects/Computing for Social Sciences/uc-cfss.github.io/data/census_bureau/cb_2013_us_state_20m/cb_2013_us_state_20m.shp' using driver `ESRI Shapefile'
## Simple feature collection with 52 features and 9 fields
## geometry type:  MULTIPOLYGON
## dimension:      XY
## bbox:           xmin: -179 ymin: 17.9 xmax: 180 ymax: 71.4
## epsg (SRID):    4269
## proj4string:    +proj=longlat +datum=NAD83 +no_defs

Draw the boundaries

ggplot(data = usa) +
  geom_sf()

Tweak the aesthetics

fiftystater

library(fiftystater)

data("fifty_states")
as_tibble(fifty_states)

## # A tibble: 13,694 x 7
##     long   lat order hole  piece id      group    
##    <dbl> <dbl> <int> <lgl> <fct> <chr>   <fct>    
##  1 -85.1  32.0     1 FALSE 1     alabama Alabama.1
##  2 -85.1  31.9     2 FALSE 1     alabama Alabama.1
##  3 -85.1  31.9     3 FALSE 1     alabama Alabama.1
##  4 -85.1  31.8     4 FALSE 1     alabama Alabama.1
##  5 -85.1  31.8     5 FALSE 1     alabama Alabama.1
##  6 -85.1  31.7     6 FALSE 1     alabama Alabama.1
##  7 -85.1  31.7     7 FALSE 1     alabama Alabama.1
##  8 -85.1  31.7     8 FALSE 1     alabama Alabama.1
##  9 -85.1  31.6     9 FALSE 1     alabama Alabama.1
## 10 -85.0  31.6    10 FALSE 1     alabama Alabama.1
## # ... with 13,684 more rows

Convert fiftystater to sf

ggplot(data = sf_fifty) +
  geom_sf()

Points

library(nycflights13)
airports

## # A tibble: 1,458 x 8
##    faa   name                   lat    lon   alt    tz dst   tzone        
##    <chr> <chr>                <dbl>  <dbl> <int> <dbl> <chr> <chr>        
##  1 04G   Lansdowne Airport     41.1  -80.6  1044    -5 A     America/New_…
##  2 06A   Moton Field Municip…  32.5  -85.7   264    -6 A     America/Chic…
##  3 06C   Schaumburg Regional   42.0  -88.1   801    -6 A     America/Chic…
##  4 06N   Randall Airport       41.4  -74.4   523    -5 A     America/New_…
##  5 09J   Jekyll Island Airpo…  31.1  -81.4    11    -5 A     America/New_…
##  6 0A9   Elizabethton Munici…  36.4  -82.2  1593    -5 A     America/New_…
##  7 0G6   Williams County Air…  41.5  -84.5   730    -5 A     America/New_…
##  8 0G7   Finger Lakes Region…  42.9  -76.8   492    -5 A     America/New_…
##  9 0P2   Shoestring Aviation…  39.8  -76.6  1000    -5 U     America/New_…
## 10 0S9   Jefferson County In…  48.1 -123.    108    -8 A     America/Los_…
## # ... with 1,448 more rows

Points

ggplot(airports, aes(lon, lat)) +
  geom_point()

Points

ggplot(data = usa_48) + 
  geom_sf() + 
  geom_point(data = airports, aes(x = lon, y = lat), shape = 1)

Points

ggplot(data = usa_48) + 
  geom_sf() + 
  geom_point(data = airports, aes(x = lon, y = lat), shape = 1) +
  coord_sf(xlim = c(-130, -60),
           ylim = c(20, 50))

Points

airports_sf <- st_as_sf(airports, coords = c("lon", "lat"))
st_crs(airports_sf) <- 4326   # set the coordinate reference system
airports_sf

## Simple feature collection with 1458 features and 6 fields
## geometry type:  POINT
## dimension:      XY
## bbox:           xmin: -177 ymin: 19.7 xmax: 174 ymax: 72.3
## epsg (SRID):    4326
## proj4string:    +proj=longlat +datum=WGS84 +no_defs
## # A tibble: 1,458 x 7
##    faa   name              alt    tz dst   tzone                  geometry
##    <chr> <chr>           <int> <dbl> <chr> <chr>               <POINT [°]>
##  1 04G   Lansdowne Airp…  1044    -5 A     America/N…         (-80.6 41.1)
##  2 06A   Moton Field Mu…   264    -6 A     America/C…         (-85.7 32.5)
##  3 06C   Schaumburg Reg…   801    -6 A     America/C…           (-88.1 42)
##  4 06N   Randall Airport   523    -5 A     America/N…         (-74.4 41.4)
##  5 09J   Jekyll Island …    11    -5 A     America/N…         (-81.4 31.1)
##  6 0A9   Elizabethton M…  1593    -5 A     America/N…         (-82.2 36.4)
##  7 0G6   Williams Count…   730    -5 A     America/N…         (-84.5 41.5)
##  8 0G7   Finger Lakes R…   492    -5 A     America/N…         (-76.8 42.9)
##  9 0P2   Shoestring Avi…  1000    -5 U     America/N…         (-76.6 39.8)
## 10 0S9   Jefferson Coun…   108    -8 A     America/L…          (-123 48.1)
## # ... with 1,448 more rows

Points

ggplot() + 
  geom_sf(data = usa_48) + 
  geom_sf(data = airports_sf, shape = 1) +
  coord_sf(xlim = c(-130, -60),
           ylim = c(20, 50))

Symbols

ggplot(data = usa_48) + 
  geom_sf() + 
  geom_point(data = airports, aes(x = lon, y = lat, size = alt),
             fill = "grey", color = "black", alpha = .2) +
  coord_sf(xlim = c(-130, -60),
           ylim = c(20, 50)) +
  scale_size_area(guide = FALSE)

Symbols

airports_n <- flights %>%
  count(dest) %>%
  left_join(airports, by = c("dest" = "faa"))

ggplot(data = usa_48) + 
  geom_sf() + 
  geom_point(data = airports_n, aes(x = lon, y = lat, size = n),
             fill = "grey", color = "black", alpha = .2) +
  coord_sf(xlim = c(-130, -60),
           ylim = c(20, 50)) +
  scale_size_area(guide = FALSE)

Fill (choropleths)

(fb_state <- read_csv("../data/census_bureau/ACS_13_5YR_B05012_state/ACS_13_5YR_B05012.csv") %>%
  mutate(rate = HD01_VD03 / HD01_VD01))

## # A tibble: 51 x 10
##    GEO.id GEO.id2 `GEO.display-la… HD01_VD01 HD02_VD01 HD01_VD02 HD02_VD02
##    <chr>  <chr>   <chr>                <int> <chr>         <int>     <int>
##  1 04000… 01      Alabama            4799277 <NA>        4631045      2881
##  2 04000… 02      Alaska              720316 <NA>         669941      1262
##  3 04000… 04      Arizona            6479703 <NA>        5609835      7725
##  4 04000… 05      Arkansas           2933369 <NA>        2799972      2568
##  5 04000… 06      California        37659181 <NA>       27483342     30666
##  6 04000… 08      Colorado           5119329 <NA>        4623809      5778
##  7 04000… 09      Connecticut        3583561 <NA>        3096374      5553
##  8 04000… 10      Delaware            908446 <NA>         831683      2039
##  9 04000… 11      District of Col…    619371 <NA>         534142      2017
## 10 04000… 12      Florida           19091156 <NA>       15392410     16848
## # ... with 41 more rows, and 3 more variables: HD01_VD03 <int>,
## #   HD02_VD03 <int>, rate <dbl>

Join the data

(usa_fb <- usa_48 %>%
  left_join(fb_state, by = c("STATEFP" = "GEO.id2")))

## Simple feature collection with 48 features and 18 fields
## geometry type:  MULTIPOLYGON
## dimension:      XY
## bbox:           xmin: -125 ymin: 24.5 xmax: -66.9 ymax: 49.4
## epsg (SRID):    4269
## proj4string:    +proj=longlat +datum=NAD83 +no_defs
## First 10 features:
##    STATEFP  STATENS    AFFGEOID GEOID STUSPS        NAME LSAD    ALAND
## 1       01 01779775 0400000US01    01     AL     Alabama   00 1.31e+11
## 2       05 00068085 0400000US05    05     AR    Arkansas   00 1.35e+11
## 3       06 01779778 0400000US06    06     CA  California   00 4.03e+11
## 4       09 01779780 0400000US09    09     CT Connecticut   00 1.25e+10
## 5       12 00294478 0400000US12    12     FL     Florida   00 1.39e+11
## 6       13 01705317 0400000US13    13     GA     Georgia   00 1.49e+11
## 7       16 01779783 0400000US16    16     ID       Idaho   00 2.14e+11
## 8       17 01779784 0400000US17    17     IL    Illinois   00 1.44e+11
## 9       18 00448508 0400000US18    18     IN     Indiana   00 9.28e+10
## 10      20 00481813 0400000US20    20     KS      Kansas   00 2.12e+11
##      AWATER      GEO.id GEO.display-label HD01_VD01 HD02_VD01 HD01_VD02
## 1  4.59e+09 0400000US01           Alabama   4799277      <NA>   4631045
## 2  2.96e+09 0400000US05          Arkansas   2933369      <NA>   2799972
## 3  2.05e+10 0400000US06        California  37659181      <NA>  27483342
## 4  1.82e+09 0400000US09       Connecticut   3583561      <NA>   3096374
## 5  3.14e+10 0400000US12           Florida  19091156      <NA>  15392410
## 6  4.95e+09 0400000US13           Georgia   9810417      <NA>   8859747
## 7  2.40e+09 0400000US16             Idaho   1583364      <NA>   1489560
## 8  6.20e+09 0400000US17          Illinois  12848554      <NA>  11073828
## 9  1.54e+09 0400000US18           Indiana   6514861      <NA>   6206801
## 10 1.35e+09 0400000US20            Kansas   2868107      <NA>   2677007
##    HD02_VD02 HD01_VD03 HD02_VD03   rate                       geometry
## 1       2881    168232      2881 0.0351 MULTIPOLYGON (((-88.3 30.2,...
## 2       2568    133397      2568 0.0455 MULTIPOLYGON (((-94.6 36.5,...
## 3      30666  10175839     30666 0.2702 MULTIPOLYGON (((-119 33.5, ...
## 4       5553    487187      5553 0.1360 MULTIPOLYGON (((-73.7 41.1,...
## 5      16848   3698746     16848 0.1937 MULTIPOLYGON (((-80.7 24.9,...
## 6       7988    950670      7988 0.0969 MULTIPOLYGON (((-85.6 35, -...
## 7       2528     93804      2528 0.0592 MULTIPOLYGON (((-117 44.4, ...
## 8      10091   1774726     10093 0.1381 MULTIPOLYGON (((-91.5 40.2,...
## 9       4499    308060      4500 0.0473 MULTIPOLYGON (((-88.1 37.9,...
## 10      3095    191100      3100 0.0666 MULTIPOLYGON (((-102 40, -1...

Draw the map

ggplot(data = usa_fb) +
  geom_sf(aes(fill = rate))

Bin data to discrete intervals

• Continuous vs. discrete variables for color
• Collapse to a discrete variable

cut_interval()

usa_fb %>%
  mutate(rate_cut = cut_interval(rate, 6)) %>%
  ggplot() +
  geom_sf(aes(fill = rate_cut))

cut_number()

usa_fb %>%
  mutate(rate_cut = cut_number(rate, 6)) %>%
  ggplot() +
  geom_sf(aes(fill = rate_cut))

Changing map projection

Changing map projection

• Coordinate reference system
• Provide a proj4string

Select a color palette

Color Brewer

• Color Brewer

Sequential palettes

Diverging palettes

Qualitative palettes